Napredna pretraga

Pregled bibliografske jedinice broj: 781601

Fuzzy Knowledge-Based Image Annotation Refinement


Ivašić-Kos, Marina; Pobar, Miran; Ribarić, Slobodan
Fuzzy Knowledge-Based Image Annotation Refinement // Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition IPCV'15 / Arabnia, Hamid R. ; Deligiannidis, Leonidas ; Tinetti, Fernando G. (ur.).
Las Vegas, Nevada, USA: The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2015. str. 284-290 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Fuzzy Knowledge-Based Image Annotation Refinement

Autori
Ivašić-Kos, Marina ; Pobar, Miran ; Ribarić, Slobodan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition IPCV'15 / Arabnia, Hamid R. ; Deligiannidis, Leonidas ; Tinetti, Fernando G. - Las Vegas, Nevada, USA : The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2015, 284-290

ISBN
1-60132-404-9

Skup
International Conference on Image Processing, Computer Vision, and Pattern Recognition IPCV'15

Mjesto i datum
Las Vegas, SAD, 27-30.7.2015

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Automatic image annotation; annotation refinement; fuzzy knowledge representation scheme; fuzzy inference

Sažetak
Automatic image annotation methods automatically assign labels to images in order to facilitate tasks such as image retrieval. Incorrect labels may negatively influence the search results so image annotation should be as accurate as possible. Labels pertaining to objects or to whole scenes are commonly used for image annotation, and precision is especially important in case when scene labels are inferred from objects, as errors in the object labels may propagate to the scene level. To improve the annotation precision, the idea is to infer which labels are incorrect using the context of other labels and the knowledge about objects and their relations. This procedure is here referred to as annotation refinement. The proposed approach used in this paper includes a fuzzy knowledge base and uses the fuzzy inference algorithms to detect and discard automatically obtained object labels that do not fit the context of other detected objects.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekt / tema
036-0361935-1954 - Teorija, modeliranje i uporaba autonomno orijentiranih računarskih struktura (Slobodan Ribarić, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb,
Sveučilište u Rijeci - Odjel za informatiku